Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations712
Missing cells550
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory122.5 KiB
Average record size in memory176.2 B

Variable types

Numeric7
Categorical12
Text3

Alerts

Age is highly overall correlated with AgeGroupHigh correlation
AgeGroup is highly overall correlated with AgeHigh correlation
Deck is highly overall correlated with PclassHigh correlation
Embarked is highly overall correlated with EmbarkedClassHigh correlation
EmbarkedClass is highly overall correlated with Embarked and 3 other fieldsHigh correlation
FamilyClass is highly overall correlated with FamilySize and 3 other fieldsHigh correlation
FamilySize is highly overall correlated with FamilyClass and 4 other fieldsHigh correlation
Fare is highly overall correlated with FamilySizeHigh correlation
FareGroup is highly overall correlated with IsAlone and 2 other fieldsHigh correlation
GenderClass is highly overall correlated with EmbarkedClass and 5 other fieldsHigh correlation
IsAlone is highly overall correlated with FamilyClass and 4 other fieldsHigh correlation
Parch is highly overall correlated with FamilyClass and 2 other fieldsHigh correlation
Pclass is highly overall correlated with Deck and 4 other fieldsHigh correlation
Sex is highly overall correlated with GenderClass and 3 other fieldsHigh correlation
SibSp is highly overall correlated with FamilyClass and 2 other fieldsHigh correlation
Survived is highly overall correlated with GenderClass and 3 other fieldsHigh correlation
Title is highly overall correlated with GenderClass and 3 other fieldsHigh correlation
TitleClass is highly overall correlated with EmbarkedClass and 6 other fieldsHigh correlation
Deck is highly imbalanced (57.3%) Imbalance
Cabin has 550 (77.2%) missing values Missing
PassengerId is uniformly distributed Uniform
PassengerId has unique values Unique
Name has unique values Unique
SibSp has 480 (67.4%) zeros Zeros
Parch has 543 (76.3%) zeros Zeros
Fare has 11 (1.5%) zeros Zeros

Reproduction

Analysis started2025-08-22 14:26:54.269261
Analysis finished2025-08-22 14:27:09.740816
Duration15.47 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct712
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.5
Minimum1
Maximum712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-22T07:27:09.912166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36.55
Q1178.75
median356.5
Q3534.25
95-th percentile676.45
Maximum712
Range711
Interquartile range (IQR)355.5

Descriptive statistics

Standard deviation205.68098
Coefficient of variation (CV)0.57694525
Kurtosis-1.2
Mean356.5
Median Absolute Deviation (MAD)178
Skewness0
Sum253828
Variance42304.667
MonotonicityStrictly increasing
2025-08-22T07:27:10.224485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
712 1
 
0.1%
1 1
 
0.1%
2 1
 
0.1%
3 1
 
0.1%
4 1
 
0.1%
5 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
696 1
 
0.1%
695 1
 
0.1%
Other values (702) 702
98.6%
ValueCountFrequency (%)
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
10 1
0.1%
ValueCountFrequency (%)
712 1
0.1%
711 1
0.1%
710 1
0.1%
709 1
0.1%
708 1
0.1%
707 1
0.1%
706 1
0.1%
705 1
0.1%
704 1
0.1%
703 1
0.1%

Survived
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
not survived
434 
survived
278 

Length

Max length12
Median length12
Mean length10.438202
Min length8

Characters and Unicode

Total characters7432
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot survived
2nd rowsurvived
3rd rowsurvived
4th rowsurvived
5th rownot survived

Common Values

ValueCountFrequency (%)
not survived 434
61.0%
survived 278
39.0%

Length

2025-08-22T07:27:10.510960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-22T07:27:10.656317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
survived 712
62.1%
not 434
37.9%

Most occurring characters

ValueCountFrequency (%)
v 1424
19.2%
s 712
9.6%
r 712
9.6%
e 712
9.6%
i 712
9.6%
d 712
9.6%
u 712
9.6%
t 434
 
5.8%
o 434
 
5.8%
n 434
 
5.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7432
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
v 1424
19.2%
s 712
9.6%
r 712
9.6%
e 712
9.6%
i 712
9.6%
d 712
9.6%
u 712
9.6%
t 434
 
5.8%
o 434
 
5.8%
n 434
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7432
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
v 1424
19.2%
s 712
9.6%
r 712
9.6%
e 712
9.6%
i 712
9.6%
d 712
9.6%
u 712
9.6%
t 434
 
5.8%
o 434
 
5.8%
n 434
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7432
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
v 1424
19.2%
s 712
9.6%
r 712
9.6%
e 712
9.6%
i 712
9.6%
d 712
9.6%
u 712
9.6%
t 434
 
5.8%
o 434
 
5.8%
n 434
 
5.8%

Pclass
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
3
390 
1
175 
2
147 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters712
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row1
5th row3

Common Values

ValueCountFrequency (%)
3 390
54.8%
1 175
24.6%
2 147
 
20.6%

Length

2025-08-22T07:27:10.862691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-22T07:27:11.014825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 390
54.8%
1 175
24.6%
2 147
 
20.6%

Most occurring characters

ValueCountFrequency (%)
3 390
54.8%
1 175
24.6%
2 147
 
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 390
54.8%
1 175
24.6%
2 147
 
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 390
54.8%
1 175
24.6%
2 147
 
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 390
54.8%
1 175
24.6%
2 147
 
20.6%

Name
Text

Unique 

Distinct712
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2025-08-22T07:27:11.788607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length82
Median length53
Mean length27.015449
Min length12

Characters and Unicode

Total characters19235
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique712 ?
Unique (%)100.0%

Sample

1st rowBraund, Mr. Owen Harris
2nd rowCumings, Mrs. John Bradley (Florence Briggs Thayer)
3rd rowHeikkinen, Miss. Laina
4th rowFutrelle, Mrs. Jacques Heath (Lily May Peel)
5th rowAllen, Mr. William Henry
ValueCountFrequency (%)
mr 417
 
14.4%
miss 155
 
5.4%
mrs 100
 
3.5%
william 53
 
1.8%
john 36
 
1.2%
henry 31
 
1.1%
master 28
 
1.0%
charles 21
 
0.7%
james 20
 
0.7%
george 18
 
0.6%
Other values (1262) 2014
69.6%
2025-08-22T07:27:12.892740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2182
 
11.3%
r 1561
 
8.1%
e 1354
 
7.0%
a 1326
 
6.9%
i 1089
 
5.7%
n 1048
 
5.4%
s 1043
 
5.4%
M 907
 
4.7%
l 860
 
4.5%
o 798
 
4.1%
Other values (50) 7067
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19235
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2182
 
11.3%
r 1561
 
8.1%
e 1354
 
7.0%
a 1326
 
6.9%
i 1089
 
5.7%
n 1048
 
5.4%
s 1043
 
5.4%
M 907
 
4.7%
l 860
 
4.5%
o 798
 
4.1%
Other values (50) 7067
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19235
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2182
 
11.3%
r 1561
 
8.1%
e 1354
 
7.0%
a 1326
 
6.9%
i 1089
 
5.7%
n 1048
 
5.4%
s 1043
 
5.4%
M 907
 
4.7%
l 860
 
4.5%
o 798
 
4.1%
Other values (50) 7067
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19235
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2182
 
11.3%
r 1561
 
8.1%
e 1354
 
7.0%
a 1326
 
6.9%
i 1089
 
5.7%
n 1048
 
5.4%
s 1043
 
5.4%
M 907
 
4.7%
l 860
 
4.5%
o 798
 
4.1%
Other values (50) 7067
36.7%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
male
456 
female
256 

Length

Max length6
Median length4
Mean length4.7191011
Min length4

Characters and Unicode

Total characters3360
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
male 456
64.0%
female 256
36.0%

Length

2025-08-22T07:27:13.109065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-22T07:27:13.253356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male 456
64.0%
female 256
36.0%

Most occurring characters

ValueCountFrequency (%)
e 968
28.8%
m 712
21.2%
a 712
21.2%
l 712
21.2%
f 256
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 968
28.8%
m 712
21.2%
a 712
21.2%
l 712
21.2%
f 256
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 968
28.8%
m 712
21.2%
a 712
21.2%
l 712
21.2%
f 256
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 968
28.8%
m 712
21.2%
a 712
21.2%
l 712
21.2%
f 256
 
7.6%

Age
Real number (ℝ)

High correlation 

Distinct84
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.369031
Minimum0.75
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-22T07:27:13.461639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.75
5-th percentile5.5
Q121
median26
Q337
95-th percentile55
Maximum80
Range79.25
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.578078
Coefficient of variation (CV)0.46232639
Kurtosis0.51411154
Mean29.369031
Median Absolute Deviation (MAD)8
Skewness0.49319024
Sum20910.75
Variance184.3642
MonotonicityNot monotonic
2025-08-22T07:27:13.752111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 86
 
12.1%
17.5 29
 
4.1%
24 28
 
3.9%
42 27
 
3.8%
22 26
 
3.7%
21 21
 
2.9%
19 21
 
2.9%
30 21
 
2.9%
28 21
 
2.9%
18 20
 
2.8%
Other values (74) 412
57.9%
ValueCountFrequency (%)
0.75 2
 
0.3%
0.83 1
 
0.1%
0.92 1
 
0.1%
1 5
0.7%
2 9
1.3%
3 6
0.8%
4 7
1.0%
5 3
 
0.4%
5.5 4
0.6%
7 3
 
0.4%
ValueCountFrequency (%)
80 1
 
0.1%
71 2
0.3%
70.5 1
 
0.1%
70 1
 
0.1%
66 1
 
0.1%
65 3
0.4%
64 2
0.3%
63 2
0.3%
62 3
0.4%
61 3
0.4%

SibSp
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52808989
Minimum0
Maximum8
Zeros480
Zeros (%)67.4%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-22T07:27:13.968435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0643423
Coefficient of variation (CV)2.0154566
Kurtosis15.989177
Mean0.52808989
Median Absolute Deviation (MAD)0
Skewness3.4428225
Sum376
Variance1.1328245
MonotonicityNot monotonic
2025-08-22T07:27:14.160755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 480
67.4%
1 169
 
23.7%
2 26
 
3.7%
3 14
 
2.0%
4 14
 
2.0%
5 5
 
0.7%
8 4
 
0.6%
ValueCountFrequency (%)
0 480
67.4%
1 169
 
23.7%
2 26
 
3.7%
3 14
 
2.0%
4 14
 
2.0%
5 5
 
0.7%
8 4
 
0.6%
ValueCountFrequency (%)
8 4
 
0.6%
5 5
 
0.7%
4 14
 
2.0%
3 14
 
2.0%
2 26
 
3.7%
1 169
 
23.7%
0 480
67.4%

Parch
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38202247
Minimum0
Maximum6
Zeros543
Zeros (%)76.3%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-22T07:27:14.334838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.81312189
Coefficient of variation (CV)2.1284661
Kurtosis10.19931
Mean0.38202247
Median Absolute Deviation (MAD)0
Skewness2.8007415
Sum272
Variance0.66116721
MonotonicityNot monotonic
2025-08-22T07:27:14.912780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 543
76.3%
1 92
 
12.9%
2 66
 
9.3%
5 4
 
0.6%
4 4
 
0.6%
3 2
 
0.3%
6 1
 
0.1%
ValueCountFrequency (%)
0 543
76.3%
1 92
 
12.9%
2 66
 
9.3%
3 2
 
0.3%
4 4
 
0.6%
5 4
 
0.6%
6 1
 
0.1%
ValueCountFrequency (%)
6 1
 
0.1%
5 4
 
0.6%
4 4
 
0.6%
3 2
 
0.3%
2 66
 
9.3%
1 92
 
12.9%
0 543
76.3%

Ticket
Text

Distinct566
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
2025-08-22T07:27:15.611227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.8019663
Min length3

Characters and Unicode

Total characters4843
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique470 ?
Unique (%)66.0%

Sample

1st rowA/5 21171
2nd rowPC 17599
3rd rowSTON/O2. 3101282
4th row113803
5th row373450
ValueCountFrequency (%)
pc 52
 
5.7%
c.a 23
 
2.5%
a/5 17
 
1.9%
2 11
 
1.2%
ca 11
 
1.2%
ston/o 11
 
1.2%
sc/paris 8
 
0.9%
soton/o.q 7
 
0.8%
w./c 6
 
0.7%
2144 6
 
0.7%
Other values (591) 763
83.4%
2025-08-22T07:27:16.556142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 607
12.5%
1 569
11.7%
2 465
9.6%
7 389
 
8.0%
4 378
 
7.8%
6 330
 
6.8%
0 319
 
6.6%
5 309
 
6.4%
9 266
 
5.5%
8 216
 
4.5%
Other values (25) 995
20.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4843
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 607
12.5%
1 569
11.7%
2 465
9.6%
7 389
 
8.0%
4 378
 
7.8%
6 330
 
6.8%
0 319
 
6.6%
5 309
 
6.4%
9 266
 
5.5%
8 216
 
4.5%
Other values (25) 995
20.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4843
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 607
12.5%
1 569
11.7%
2 465
9.6%
7 389
 
8.0%
4 378
 
7.8%
6 330
 
6.8%
0 319
 
6.6%
5 309
 
6.4%
9 266
 
5.5%
8 216
 
4.5%
Other values (25) 995
20.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4843
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 607
12.5%
1 569
11.7%
2 465
9.6%
7 389
 
8.0%
4 378
 
7.8%
6 330
 
6.8%
0 319
 
6.6%
5 309
 
6.4%
9 266
 
5.5%
8 216
 
4.5%
Other values (25) 995
20.5%

Fare
Real number (ℝ)

High correlation  Zeros 

Distinct228
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.509538
Minimum0
Maximum512.3292
Zeros11
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-22T07:27:16.786569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.225
Q17.925
median15.0229
Q331.275
95-th percentile113.275
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.35

Descriptive statistics

Standard deviation48.67271
Coefficient of variation (CV)1.4971825
Kurtosis31.311056
Mean32.509538
Median Absolute Deviation (MAD)7.3833
Skewness4.5799705
Sum23146.791
Variance2369.0327
MonotonicityNot monotonic
2025-08-22T07:27:17.073014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.05 40
 
5.6%
13 30
 
4.2%
7.8958 29
 
4.1%
26 27
 
3.8%
7.75 27
 
3.8%
10.5 19
 
2.7%
26.55 14
 
2.0%
7.925 14
 
2.0%
7.25 12
 
1.7%
7.8542 11
 
1.5%
Other values (218) 489
68.7%
ValueCountFrequency (%)
0 11
1.5%
4.0125 1
 
0.1%
6.2375 1
 
0.1%
6.4958 2
 
0.3%
6.75 2
 
0.3%
6.8583 1
 
0.1%
6.975 1
 
0.1%
7.0458 1
 
0.1%
7.05 5
0.7%
7.0542 1
 
0.1%
ValueCountFrequency (%)
512.3292 2
0.3%
263 4
0.6%
262.375 1
 
0.1%
247.5208 2
0.3%
227.525 3
0.4%
221.7792 1
 
0.1%
211.5 1
 
0.1%
211.3375 1
 
0.1%
164.8667 1
 
0.1%
153.4625 3
0.4%

Cabin
Text

Missing 

Distinct122
Distinct (%)75.3%
Missing550
Missing (%)77.2%
Memory size5.7 KiB
2025-08-22T07:27:18.031963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length3
Mean length3.5185185
Min length1

Characters and Unicode

Total characters570
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique90 ?
Unique (%)55.6%

Sample

1st rowC85
2nd rowC123
3rd rowE46
4th rowG6
5th rowC103
ValueCountFrequency (%)
c23 4
 
2.1%
c25 4
 
2.1%
c27 4
 
2.1%
g6 4
 
2.1%
f33 3
 
1.6%
c22 3
 
1.6%
c26 3
 
1.6%
d 3
 
1.6%
f2 3
 
1.6%
f 3
 
1.6%
Other values (125) 153
81.8%
2025-08-22T07:27:19.183016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 64
11.2%
2 59
 
10.4%
3 51
 
8.9%
1 44
 
7.7%
6 41
 
7.2%
B 41
 
7.2%
5 34
 
6.0%
4 28
 
4.9%
8 27
 
4.7%
D 26
 
4.6%
Other values (9) 155
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 570
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 64
11.2%
2 59
 
10.4%
3 51
 
8.9%
1 44
 
7.7%
6 41
 
7.2%
B 41
 
7.2%
5 34
 
6.0%
4 28
 
4.9%
8 27
 
4.7%
D 26
 
4.6%
Other values (9) 155
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 570
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 64
11.2%
2 59
 
10.4%
3 51
 
8.9%
1 44
 
7.7%
6 41
 
7.2%
B 41
 
7.2%
5 34
 
6.0%
4 28
 
4.9%
8 27
 
4.7%
D 26
 
4.6%
Other values (9) 155
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 570
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 64
11.2%
2 59
 
10.4%
3 51
 
8.9%
1 44
 
7.7%
6 41
 
7.2%
B 41
 
7.2%
5 34
 
6.0%
4 28
 
4.9%
8 27
 
4.7%
D 26
 
4.6%
Other values (9) 155
27.2%

Embarked
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
S
510 
C
138 
Q
64 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters712
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS
2nd rowC
3rd rowS
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S 510
71.6%
C 138
 
19.4%
Q 64
 
9.0%

Length

2025-08-22T07:27:19.400274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-22T07:27:19.549420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s 510
71.6%
c 138
 
19.4%
q 64
 
9.0%

Most occurring characters

ValueCountFrequency (%)
S 510
71.6%
C 138
 
19.4%
Q 64
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 510
71.6%
C 138
 
19.4%
Q 64
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 510
71.6%
C 138
 
19.4%
Q 64
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 510
71.6%
C 138
 
19.4%
Q 64
 
9.0%

Title
Categorical

High correlation 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Mr
413 
Miss
155 
Mrs
96 
Master
 
28
Rare
 
20

Length

Max length6
Median length2
Mean length2.7837079
Min length2

Characters and Unicode

Total characters1982
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMr
2nd rowMrs
3rd rowMiss
4th rowMrs
5th rowMr

Common Values

ValueCountFrequency (%)
Mr 413
58.0%
Miss 155
 
21.8%
Mrs 96
 
13.5%
Master 28
 
3.9%
Rare 20
 
2.8%

Length

2025-08-22T07:27:19.748658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-22T07:27:19.917894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mr 413
58.0%
miss 155
 
21.8%
mrs 96
 
13.5%
master 28
 
3.9%
rare 20
 
2.8%

Most occurring characters

ValueCountFrequency (%)
M 692
34.9%
r 557
28.1%
s 434
21.9%
i 155
 
7.8%
a 48
 
2.4%
e 48
 
2.4%
t 28
 
1.4%
R 20
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 692
34.9%
r 557
28.1%
s 434
21.9%
i 155
 
7.8%
a 48
 
2.4%
e 48
 
2.4%
t 28
 
1.4%
R 20
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 692
34.9%
r 557
28.1%
s 434
21.9%
i 155
 
7.8%
a 48
 
2.4%
e 48
 
2.4%
t 28
 
1.4%
R 20
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 692
34.9%
r 557
28.1%
s 434
21.9%
i 155
 
7.8%
a 48
 
2.4%
e 48
 
2.4%
t 28
 
1.4%
R 20
 
1.0%

FamilySize
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9101124
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-22T07:27:20.098195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5788008
Coefficient of variation (CV)0.82654867
Kurtosis8.2223885
Mean1.9101124
Median Absolute Deviation (MAD)0
Skewness2.597848
Sum1360
Variance2.4926121
MonotonicityNot monotonic
2025-08-22T07:27:20.269500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 422
59.3%
2 133
 
18.7%
3 86
 
12.1%
4 21
 
2.9%
6 18
 
2.5%
5 12
 
1.7%
7 10
 
1.4%
8 6
 
0.8%
11 4
 
0.6%
ValueCountFrequency (%)
1 422
59.3%
2 133
 
18.7%
3 86
 
12.1%
4 21
 
2.9%
5 12
 
1.7%
6 18
 
2.5%
7 10
 
1.4%
8 6
 
0.8%
11 4
 
0.6%
ValueCountFrequency (%)
11 4
 
0.6%
8 6
 
0.8%
7 10
 
1.4%
6 18
 
2.5%
5 12
 
1.7%
4 21
 
2.9%
3 86
 
12.1%
2 133
 
18.7%
1 422
59.3%

IsAlone
Categorical

High correlation 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
1
422 
0
290 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters712
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 422
59.3%
0 290
40.7%

Length

2025-08-22T07:27:20.478775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-22T07:27:20.613901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 422
59.3%
0 290
40.7%

Most occurring characters

ValueCountFrequency (%)
1 422
59.3%
0 290
40.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 422
59.3%
0 290
40.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 422
59.3%
0 290
40.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 422
59.3%
0 290
40.7%

Deck
Categorical

High correlation  Imbalance 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
U
550 
C
 
52
B
 
32
D
 
25
E
 
24
Other values (4)
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters712
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowU
2nd rowC
3rd rowU
4th rowC
5th rowU

Common Values

ValueCountFrequency (%)
U 550
77.2%
C 52
 
7.3%
B 32
 
4.5%
D 25
 
3.5%
E 24
 
3.4%
A 13
 
1.8%
F 11
 
1.5%
G 4
 
0.6%
T 1
 
0.1%

Length

2025-08-22T07:27:20.785257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-22T07:27:20.982555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
u 550
77.2%
c 52
 
7.3%
b 32
 
4.5%
d 25
 
3.5%
e 24
 
3.4%
a 13
 
1.8%
f 11
 
1.5%
g 4
 
0.6%
t 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
U 550
77.2%
C 52
 
7.3%
B 32
 
4.5%
D 25
 
3.5%
E 24
 
3.4%
A 13
 
1.8%
F 11
 
1.5%
G 4
 
0.6%
T 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 550
77.2%
C 52
 
7.3%
B 32
 
4.5%
D 25
 
3.5%
E 24
 
3.4%
A 13
 
1.8%
F 11
 
1.5%
G 4
 
0.6%
T 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 550
77.2%
C 52
 
7.3%
B 32
 
4.5%
D 25
 
3.5%
E 24
 
3.4%
A 13
 
1.8%
F 11
 
1.5%
G 4
 
0.6%
T 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 712
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 550
77.2%
C 52
 
7.3%
B 32
 
4.5%
D 25
 
3.5%
E 24
 
3.4%
A 13
 
1.8%
F 11
 
1.5%
G 4
 
0.6%
T 1
 
0.1%

AgeGroup
Categorical

High correlation 

Distinct5
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Young Adult
373 
Adult
183 
Teenager
82 
Child
55 
Senior
 
19

Length

Max length11
Median length11
Mean length8.5154494
Min length5

Characters and Unicode

Total characters6063
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYoung Adult
2nd rowAdult
3rd rowYoung Adult
4th rowYoung Adult
5th rowYoung Adult

Common Values

ValueCountFrequency (%)
Young Adult 373
52.4%
Adult 183
25.7%
Teenager 82
 
11.5%
Child 55
 
7.7%
Senior 19
 
2.7%

Length

2025-08-22T07:27:21.238779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-22T07:27:21.422084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
adult 556
51.2%
young 373
34.4%
teenager 82
 
7.6%
child 55
 
5.1%
senior 19
 
1.8%

Most occurring characters

ValueCountFrequency (%)
u 929
15.3%
d 611
10.1%
l 611
10.1%
A 556
9.2%
t 556
9.2%
n 474
7.8%
g 455
7.5%
o 392
6.5%
Y 373
6.2%
373
6.2%
Other values (8) 733
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 929
15.3%
d 611
10.1%
l 611
10.1%
A 556
9.2%
t 556
9.2%
n 474
7.8%
g 455
7.5%
o 392
6.5%
Y 373
6.2%
373
6.2%
Other values (8) 733
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 929
15.3%
d 611
10.1%
l 611
10.1%
A 556
9.2%
t 556
9.2%
n 474
7.8%
g 455
7.5%
o 392
6.5%
Y 373
6.2%
373
6.2%
Other values (8) 733
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 929
15.3%
d 611
10.1%
l 611
10.1%
A 556
9.2%
t 556
9.2%
n 474
7.8%
g 455
7.5%
o 392
6.5%
Y 373
6.2%
373
6.2%
Other values (8) 733
12.1%

FareGroup
Categorical

High correlation 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Low
186 
Medium-High
180 
High
176 
Medium-Low
170 

Length

Max length11
Median length10
Mean length6.9410112
Min length3

Characters and Unicode

Total characters4942
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowHigh
3rd rowLow
4th rowHigh
5th rowMedium-Low

Common Values

ValueCountFrequency (%)
Low 186
26.1%
Medium-High 180
25.3%
High 176
24.7%
Medium-Low 170
23.9%

Length

2025-08-22T07:27:21.671586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-22T07:27:21.855881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 186
26.1%
medium-high 180
25.3%
high 176
24.7%
medium-low 170
23.9%

Most occurring characters

ValueCountFrequency (%)
i 706
14.3%
o 356
 
7.2%
w 356
 
7.2%
h 356
 
7.2%
L 356
 
7.2%
g 356
 
7.2%
H 356
 
7.2%
e 350
 
7.1%
M 350
 
7.1%
m 350
 
7.1%
Other values (3) 1050
21.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 706
14.3%
o 356
 
7.2%
w 356
 
7.2%
h 356
 
7.2%
L 356
 
7.2%
g 356
 
7.2%
H 356
 
7.2%
e 350
 
7.1%
M 350
 
7.1%
m 350
 
7.1%
Other values (3) 1050
21.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 706
14.3%
o 356
 
7.2%
w 356
 
7.2%
h 356
 
7.2%
L 356
 
7.2%
g 356
 
7.2%
H 356
 
7.2%
e 350
 
7.1%
M 350
 
7.1%
m 350
 
7.1%
Other values (3) 1050
21.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 706
14.3%
o 356
 
7.2%
w 356
 
7.2%
h 356
 
7.2%
L 356
 
7.2%
g 356
 
7.2%
H 356
 
7.2%
e 350
 
7.1%
M 350
 
7.1%
m 350
 
7.1%
Other values (3) 1050
21.2%

EmbarkedClass
Categorical

High correlation 

Distinct9
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
S3
279 
S2
131 
S1
100 
C1
73 
Q3
59 
Other values (4)
70 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1424
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS3
2nd rowC1
3rd rowS3
4th rowS1
5th rowS3

Common Values

ValueCountFrequency (%)
S3 279
39.2%
S2 131
18.4%
S1 100
 
14.0%
C1 73
 
10.3%
Q3 59
 
8.3%
C3 52
 
7.3%
C2 13
 
1.8%
Q2 3
 
0.4%
Q1 2
 
0.3%

Length

2025-08-22T07:27:22.080127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-22T07:27:22.283452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s3 279
39.2%
s2 131
18.4%
s1 100
 
14.0%
c1 73
 
10.3%
q3 59
 
8.3%
c3 52
 
7.3%
c2 13
 
1.8%
q2 3
 
0.4%
q1 2
 
0.3%

Most occurring characters

ValueCountFrequency (%)
S 510
35.8%
3 390
27.4%
1 175
 
12.3%
2 147
 
10.3%
C 138
 
9.7%
Q 64
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 510
35.8%
3 390
27.4%
1 175
 
12.3%
2 147
 
10.3%
C 138
 
9.7%
Q 64
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 510
35.8%
3 390
27.4%
1 175
 
12.3%
2 147
 
10.3%
C 138
 
9.7%
Q 64
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 510
35.8%
3 390
27.4%
1 175
 
12.3%
2 147
 
10.3%
C 138
 
9.7%
Q 64
 
4.5%

GenderClass
Categorical

High correlation 

Distinct6
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
male3
269 
female3
121 
male1
102 
male2
85 
female1
73 

Length

Max length7
Median length5
Mean length5.7191011
Min length5

Characters and Unicode

Total characters4072
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale3
2nd rowfemale1
3rd rowfemale3
4th rowfemale1
5th rowmale3

Common Values

ValueCountFrequency (%)
male3 269
37.8%
female3 121
17.0%
male1 102
 
14.3%
male2 85
 
11.9%
female1 73
 
10.3%
female2 62
 
8.7%

Length

2025-08-22T07:27:22.576169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-22T07:27:22.768654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male3 269
37.8%
female3 121
17.0%
male1 102
 
14.3%
male2 85
 
11.9%
female1 73
 
10.3%
female2 62
 
8.7%

Most occurring characters

ValueCountFrequency (%)
e 968
23.8%
m 712
17.5%
a 712
17.5%
l 712
17.5%
3 390
9.6%
f 256
 
6.3%
1 175
 
4.3%
2 147
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 968
23.8%
m 712
17.5%
a 712
17.5%
l 712
17.5%
3 390
9.6%
f 256
 
6.3%
1 175
 
4.3%
2 147
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 968
23.8%
m 712
17.5%
a 712
17.5%
l 712
17.5%
3 390
9.6%
f 256
 
6.3%
1 175
 
4.3%
2 147
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 968
23.8%
m 712
17.5%
a 712
17.5%
l 712
17.5%
3 390
9.6%
f 256
 
6.3%
1 175
 
4.3%
2 147
 
3.6%

TitleClass
Categorical

High correlation 

Distinct14
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size5.7 KiB
Mr3
249 
Mr1
91 
Miss3
87 
Mr2
73 
Miss1
39 
Other values (9)
173 

Length

Max length7
Median length3
Mean length3.7837079
Min length3

Characters and Unicode

Total characters2694
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMr3
2nd rowMrs1
3rd rowMiss3
4th rowMrs1
5th rowMr3

Common Values

ValueCountFrequency (%)
Mr3 249
35.0%
Mr1 91
 
12.8%
Miss3 87
 
12.2%
Mr2 73
 
10.3%
Miss1 39
 
5.5%
Mrs3 34
 
4.8%
Mrs2 32
 
4.5%
Mrs1 30
 
4.2%
Miss2 29
 
4.1%
Master3 20
 
2.8%
Other values (4) 28
 
3.9%

Length

2025-08-22T07:27:23.037896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mr3 249
35.0%
mr1 91
 
12.8%
miss3 87
 
12.2%
mr2 73
 
10.3%
miss1 39
 
5.5%
mrs3 34
 
4.8%
mrs2 32
 
4.5%
mrs1 30
 
4.2%
miss2 29
 
4.1%
master3 20
 
2.8%
Other values (4) 28
 
3.9%

Most occurring characters

ValueCountFrequency (%)
M 692
25.7%
r 557
20.7%
s 434
16.1%
3 390
14.5%
1 175
 
6.5%
i 155
 
5.8%
2 147
 
5.5%
a 48
 
1.8%
e 48
 
1.8%
t 28
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 692
25.7%
r 557
20.7%
s 434
16.1%
3 390
14.5%
1 175
 
6.5%
i 155
 
5.8%
2 147
 
5.5%
a 48
 
1.8%
e 48
 
1.8%
t 28
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 692
25.7%
r 557
20.7%
s 434
16.1%
3 390
14.5%
1 175
 
6.5%
i 155
 
5.8%
2 147
 
5.5%
a 48
 
1.8%
e 48
 
1.8%
t 28
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 692
25.7%
r 557
20.7%
s 434
16.1%
3 390
14.5%
1 175
 
6.5%
i 155
 
5.8%
2 147
 
5.5%
a 48
 
1.8%
e 48
 
1.8%
t 28
 
1.0%

FamilyClass
Real number (ℝ)

High correlation 

Distinct20
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.40309
Minimum11
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 KiB
2025-08-22T07:27:23.236272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q113
median13
Q323
95-th percentile61
Maximum113
Range102
Interquartile range (IQR)10

Descriptive statistics

Standard deviation15.869161
Coefficient of variation (CV)0.74144251
Kurtosis8.3934816
Mean21.40309
Median Absolute Deviation (MAD)2
Skewness2.6305272
Sum15239
Variance251.83026
MonotonicityNot monotonic
2025-08-22T07:27:23.441538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
13 251
35.3%
11 87
 
12.2%
12 84
 
11.8%
21 59
 
8.3%
23 48
 
6.7%
33 42
 
5.9%
22 26
 
3.7%
32 25
 
3.5%
31 19
 
2.7%
63 13
 
1.8%
Other values (10) 58
 
8.1%
ValueCountFrequency (%)
11 87
 
12.2%
12 84
 
11.8%
13 251
35.3%
21 59
 
8.3%
22 26
 
3.7%
23 48
 
6.7%
31 19
 
2.7%
32 25
 
3.5%
33 42
 
5.9%
41 5
 
0.7%
ValueCountFrequency (%)
113 4
 
0.6%
83 6
0.8%
73 10
1.4%
63 13
1.8%
62 1
 
0.1%
61 4
 
0.6%
53 11
1.5%
51 1
 
0.1%
43 5
 
0.7%
42 11
1.5%

Interactions

2025-08-22T07:27:07.332556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:57.307854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:58.920388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:00.577218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:02.271787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:03.864703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:05.565431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:07.586161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:57.494153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:59.182932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:00.838722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:02.509815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:04.097284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:05.848775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:07.835196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:57.738279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:59.427152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:01.093750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:02.757643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:04.328121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:06.112219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:08.068640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:57.982689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:59.674720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:01.339553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:02.989061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:04.557563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:06.377739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:08.293880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:58.216425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:59.907027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:01.573795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:03.210398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:04.779944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:06.636985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:08.513161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:58.462975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:00.139123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:01.812952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:03.432678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:04.981627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:06.900749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:08.722379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:26:58.696502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:00.358600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:02.034965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:03.654633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:05.262394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-22T07:27:07.135219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-22T07:27:23.653054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAgeGroupDeckEmbarkedEmbarkedClassFamilyClassFamilySizeFareFareGroupGenderClassIsAloneParchPassengerIdPclassSexSibSpSurvivedTitleTitleClass
Age1.0000.8080.1670.1090.164-0.356-0.2210.1740.2280.2520.378-0.2580.0900.3070.291-0.1890.2170.4180.320
AgeGroup0.8081.0000.1850.1940.2520.2860.2740.0900.2180.2920.3790.2840.0750.3140.2730.2570.1320.4320.496
Deck0.1670.1851.0000.2180.3130.0490.0000.2900.3560.3930.1890.0000.0000.5980.1590.0000.2940.1370.339
Embarked0.1090.1940.2181.0000.9960.0960.0640.2150.2600.2990.1190.0500.0000.2710.1280.0890.1540.1600.315
EmbarkedClass0.1640.2520.3130.9961.0000.1230.1010.2920.4770.6360.1420.0000.0000.9960.1670.0930.3390.1800.515
FamilyClass-0.3560.2860.0490.0960.1231.0000.9070.2000.3110.1750.8300.714-0.0610.2100.2400.7710.1990.2780.241
FamilySize-0.2210.2740.0000.0640.1010.9071.0000.5160.2550.1290.6340.793-0.0450.1470.1860.8430.2040.2420.220
Fare0.1740.0900.2900.2150.2920.2000.5161.0000.4660.3300.2850.3920.0270.4890.1560.4310.2660.0670.307
FareGroup0.2280.2180.3560.2600.4770.3110.2550.4661.0000.4900.5820.2420.0030.5650.2020.3010.2830.2050.526
GenderClass0.2520.2920.3930.2990.6360.1750.1290.3300.4901.0000.2940.1020.0680.9980.9970.1140.6160.5130.985
IsAlone0.3780.3790.1890.1190.1420.8300.6340.2850.5820.2941.0000.6670.0000.1130.2790.8340.1670.4660.478
Parch-0.2580.2840.0000.0500.0000.7140.7930.3920.2420.1020.6671.000-0.0120.0000.2260.4260.1170.2420.226
PassengerId0.0900.0750.0000.0000.000-0.061-0.0450.0270.0030.0680.000-0.0121.0000.0090.079-0.0530.0240.0570.049
Pclass0.3070.3140.5980.2710.9960.2100.1470.4890.5650.9980.1130.0000.0091.0000.1000.1370.3150.1770.992
Sex0.2910.2730.1590.1280.1670.2400.1860.1560.2020.9970.2790.2260.0790.1001.0000.1890.5360.9860.980
SibSp-0.1890.2570.0000.0890.0930.7710.8430.4310.3010.1140.8340.426-0.0530.1370.1891.0000.1660.2960.270
Survived0.2170.1320.2940.1540.3390.1990.2040.2660.2830.6160.1670.1170.0240.3150.5360.1661.0000.5510.633
Title0.4180.4320.1370.1600.1800.2780.2420.0670.2050.5130.4660.2420.0570.1770.9860.2960.5511.0000.994
TitleClass0.3200.4960.3390.3150.5150.2410.2200.3070.5260.9850.4780.2260.0490.9920.9800.2700.6330.9941.000

Missing values

2025-08-22T07:27:09.102031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-22T07:27:09.514575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedTitleFamilySizeIsAloneDeckAgeGroupFareGroupEmbarkedClassGenderClassTitleClassFamilyClass
01not survived3Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNSMr20UYoung AdultLowS3male3Mr323
12survived1Cumings, Mrs. John Bradley (Florence Briggs Thayer)female38.010PC 1759971.2833C85CMrs20CAdultHighC1female1Mrs121
23survived3Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNSMiss11UYoung AdultLowS3female3Miss313
34survived1Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123SMrs20CYoung AdultHighS1female1Mrs121
45not survived3Allen, Mr. William Henrymale35.0003734508.0500NaNSMr11UYoung AdultMedium-LowS3male3Mr313
56not survived3Moran, Mr. Jamesmale26.0003308778.4583NaNQMr11UYoung AdultMedium-LowQ3male3Mr313
67not survived1McCarthy, Mr. Timothy Jmale54.0001746351.8625E46SMr11EAdultHighS1male1Mr111
78not survived3Palsson, Master. Gosta Leonardmale2.03134990921.0750NaNSMaster50UChildMedium-HighS3male3Master353
89survived3Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.00234774211.1333NaNSMrs30UYoung AdultMedium-LowS3female3Mrs333
910survived2Nasser, Mrs. Nicholas (Adele Achem)female14.01023773630.0708NaNCMrs20UTeenagerMedium-HighC2female2Mrs222
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedTitleFamilySizeIsAloneDeckAgeGroupFareGroupEmbarkedClassGenderClassTitleClassFamilyClass
702703not survived3Barbara, Miss. Saiidefemale18.001269114.4542NaNCMiss20UTeenagerMedium-LowC3female3Miss323
703704not survived3Gallagher, Mr. Martinmale25.000368647.7417NaNQMr11UYoung AdultLowQ3male3Mr313
704705not survived3Hansen, Mr. Henrik Juulmale26.0103500257.8542NaNSMr20UYoung AdultLowS3male3Mr323
705706not survived2Morley, Mr. Henry Samuel ("Mr Henry Marshall")male39.00025065526.0000NaNSMr11UAdultMedium-HighS2male2Mr212
706707survived2Kelly, Mrs. Florence "Fannie"female45.00022359613.5000NaNSMrs11UAdultMedium-LowS2female2Mrs212
707708survived1Calderhead, Mr. Edward Penningtonmale42.000PC 1747626.2875E24SMr11EAdultMedium-HighS1male1Mr111
708709survived1Cleaver, Miss. Alicefemale22.000113781151.5500NaNSMiss11UYoung AdultHighS1female1Miss111
709710survived3Moubarek, Master. Halim Gonios ("William George")male5.511266115.2458NaNCMaster30UChildMedium-HighC3male3Master333
710711survived1Mayne, Mlle. Berthe Antonine ("Mrs de Villiers")female24.000PC 1748249.5042C90CRare11CYoung AdultHighC1female1Rare111
711712not survived1Klaber, Mr. Hermanmale42.00011302826.5500C124SMr11CAdultMedium-HighS1male1Mr111